Quantifying the Effect of Precipitation on Landslide Hazard in Urbanized and Non‐Urbanized Areas

Johnston, Elizabeth; Davenport, Frances V.; Wang, Lijing; Caers, Jef; Muthukrishnan, Suresh; Burke, Marshall; Diffenbaugh, Noah S.

doi:10.1029/2021gl094038

Cited by 31 publications

(9 citation statements)

References 81 publications

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“…One example of this is in open pit mining (e.g., Bingham Canyon mine; Gaida et al., 2021). This is precisely why we designed our platform to be one complete package—with the ability to accommodate the changing conditions that may intensify certain landslide triggers (e.g., climate change that may lead to increased intensity, duration, and frequency of rainfall (Johnston et al., 2021; Justice, 2020), earthquakes (Masih, 2018, etc.). Specifically, if historical data on past failure events on the vulnerable sites are available, we recommend that our entire platform be implemented for improved decision‐making.…”

Section: Discussionmentioning

confidence: 99%

Pinpointing Early Signs of Impending Slope Failures From Space

et al. 2022

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A promised potential of spaceborne interferometric synthetic aperture radars (InSAR) is a capability for regularly monitoring ground deformation with millimeter accuracy, for timely forecasting of impending natural hazards such as landslides. The main limitation in InSAR being actually capable of unleashing this potential for hazard prediction is that key precursory ground displacements are, in the majority of cases, a very small subset of the entire big data set provided by the method over large regions. Consequently, pinpointing a single impending failure may become very difficult or impossible. We develop a data‐driven framework that can handle such imbalanced spatiotemporal data based on the concept of outlying aspects mining, to find a subset of features out of a collection of potential features, which best distinguishes the landslide source area from the others. We show that the identified feature subspace can be used to find anomalous areas across multiple spatial scales, such that Sentinel‐1 satellite monitoring points which persistently lie in these areas can accurately detect the location of the Xinmo landslide (China) almost 1 year in advance—without false alarms. In a second case study, we identify the area affected by rockfalls on Stromboli volcano, a task that is generally infeasible with traditional methods applied to InSAR data. With continuing improvements in the spatial and temporal resolution from the new generation of satellites, such as Sentinel‐1, this approach opens the door to reliable and early prediction of failure over a broad range of slope instabilities.

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Section: Discussionmentioning

confidence: 99%

Pinpointing Early Signs of Impending Slope Failures From Space

et al. 2022

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“…This apparent mismatch could arise from the physics of landslide occurrence or from the details of the recording protocol. Landslide activity in urban areas and along roads may be higher than in rural areas (Johnston et al., 2021), spatial variations in landslide susceptibility may be captured by different inventory footprints, or average landslide intensities may have differed in the time periods covered by the inventories (Lombardo et al., 2020). A more likely explanation is reporting bias arising from more detailed observations in urban areas and along roads (Steger et al., 2017).…”

Section: Discussionmentioning

confidence: 99%

Seasonal Landslide Activity Lags Annual Precipitation Pattern in the Pacific Northwest

Luna

Korup

2022

Geophysical Research Letters

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Seasonal variations in landslide activity remain understudied compared to recent advances in landslide early warning at hourly to daily timescales. Here, we learn the seasonal pattern of monthly landslide activity in the Pacific Northwest from five heterogeneous landslide inventories with differing spatial and temporal coverage and reporting protocols combined in a Bayesian multi‐level model. We find that landslide activity is distinctly seasonal, with credible increases in landslide intensity, inter‐annual variability, and probability marking the onset of the landslide season in November. Peaks in landslide probability in January and intensity in February lag the annual peak in mean monthly precipitation and landslide activity is more variable in winter than in summer, when landslides are rare. For a given monthly rainfall, landslide intensity at the season peak in February is up to 10 times higher than at the onset in November, underlining the importance of antecedent seasonal hillslope conditions.

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“…There should exist some facilitating conditions that aid the rate of strength reduction and increase failure occurrence (Johnson and Sitar 1990;Anderson and Sitar 1995;Johnston et al 2021;Natalia and Yang 2022). The presence of hydrological characteristics such as constrictive conductive zones and preferential drainage paths facilitates localized pore water development and soil strength reduction.…”

Section: Introductionmentioning

confidence: 99%

New insights into the catastrophic slope failures at Sau Mau Ping: the role of antecedent rainfall pattern

Natalia,

Yang

2024

Landslides

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Climate change is increasing the frequency and intensity of extreme rainfall events, which aggravate the threat to the safety of natural and man-made slopes. There is growing interest in the role of rainfall characteristics in these slope failures. Most of previous studies treated the rainfall individually or as cumulative value and used hypothetical rainfall temporal patterns with no association with actual physical failures. In this study, the two deadly landslides in Sau Mau Ping, Hong Kong, in June 1972 and August 1976, which caused 165 casualties, are revisited. An intriguing question that has long been overlooked is posed: why the slopes that withstood the 1972 rainfall failed in the 1976 rainfall, given that the rainfall intensity of the latter event was only half of the former? Based on an extensive review of the forensic reports and relevant studies on the failure events, numerical modeling is carried out by a combination of seepage analysis with stability analysis and unsaturated shear strength theory. Focus is placed on the geological and hydrological settings and the rainfall characteristics, particularly the temporal pattern of the antecedent and main rainfall, to look into the causes and mechanisms for these failures. Implications of the new findings for future research and practice are also highlighted.

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Quantifying the Effect of Precipitation on Landslide Hazard in Urbanized and Non‐Urbanized Areas

Cited by 31 publications

References 81 publications

Pinpointing Early Signs of Impending Slope Failures From Space

Pinpointing Early Signs of Impending Slope Failures From Space

Seasonal Landslide Activity Lags Annual Precipitation Pattern in the Pacific Northwest

New insights into the catastrophic slope failures at Sau Mau Ping: the role of antecedent rainfall pattern

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